21University of Cambridge Metabolic Research Laboratories and National Institute for Health Research Cambridge Biomedical Research
Centre, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, U.K.

Abstract

Patients with established type 2 diabetes display both β-cell dysfunction and insulin resistance. To define fundamental processes
leading to the diabetic state, we examined the relationship between type 2 diabetes risk variants at 37 established susceptibility
loci, and indices of proinsulin processing, insulin secretion, and insulin sensitivity. We included data from up to 58,614
nondiabetic subjects with basal measures and 17,327 with dynamic measures. We used additive genetic models with adjustment
for sex, age, and BMI, followed by fixed-effects, inverse-variance meta-analyses. Cluster analyses grouped risk loci into
five major categories based on their relationship to these continuous glycemic phenotypes. The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity. The second cluster (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia. ARAP1 constituted a third cluster characterized by defects in insulin processing. A fourth cluster (TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B) was defined by loci influencing insulin processing and secretion without a detectable change in fasting glucose levels.
The final group contained 20 risk loci with no clear-cut associations to continuous glycemic traits. By assembling extensive
data on continuous glycemic traits, we have exposed the diverse mechanisms whereby type 2 diabetes risk variants impact disease
predisposition.

Introduction

Type 2 diabetes is a metabolic disorder characterized by impaired insulin secretion and reduced sensitivity to the peripheral
actions of insulin. Both genetic and environmental factors contribute to the development of type 2 diabetes (1), but the fundamental mechanistic defects contributing to the evolution of disease remain far from clear. Recently, genome-wide
association efforts have extended the number of loci robustly implicated in type 2 diabetes risk to >60 (2–5). Each of these loci contains sequence variants that are causal for disease risk, and elucidation of the mechanisms through
which these loci operate has the potential to reveal processes fundamental to disease pathogenesis.

To date, systematic review of the effects of disease risk variants on processes contributing to the diabetic state has mostly
been restricted to the examination of basal indices of β-cell (BC) function or insulin sensitivity (2,3). These studies have demonstrated that most, but not all, of these loci exert their primary effects on disease risk through
deficient insulin secretion rather than insulin resistance (IR) (2,4–6).

Further dissection of these mechanisms requires more intensive phenotyping in risk allele carriers and controls. In principle,
such studies, particularly if performed in nondiabetic individuals, can provide readouts of the status of various aspects
of intermediary metabolism. For example, disproportionately raised levels of circulating fasting proinsulin (PI), compared
with those of fasting insulin (FI), reflect BC stress and impairment in early insulin processing. Fasting 32,33 split PI provides
a more detailed assessment of the impact of genetic variants on insulin processing (7). Dynamic tests of insulin secretion following oral and/or intravenous glucose administration can provide insights into early
BC dysfunction and loss of early insulin release (8). Studies of selected diabetes risk loci conducted using more refined dynamic measures of insulin secretion and/or sensitivity
have, in the main, corroborated the broad inferences obtained from basal measures (9–13), but have often been underpowered with respect to more detailed mechanistic insights. To date, the most substantial effort
to characterize the physiology of risk allele carriers involved a survey of 19 loci defined by their primary associations
with quantitative glycemic traits, such as fasting glucose (FG) and FI, 8 of which were also type 2 diabetes risk loci (14). This study underscored the heterogeneity of genetic effects on glucose homeostasis.

Here we expand upon that study to examine the effect of a total of 37 type 2 diabetes risk loci across a broad range of quantitative
measures of glycemic metabolism. To do this, within the Meta-Analyses of Glucose and Insulin-Related Traits Consortium (MAGIC),
we have gathered basal and dynamic testing data from 15 studies providing detailed measures of insulin processing (fasting
PI and 32,33 split PI), insulin secretion (insulinogenic index and acute insulin response [AIR]), insulin sensitivity (indices
derived from oral and intravenous tests of glucose-stimulated insulin secretion) (15), and insulin clearance (C-peptide), and combined these with existing published data for FG, FI, and homeostasis model assessment
(HOMA) data from the MAGIC meta-analysis of fasting traits (16). In doing so, we were able to demonstrate that the mechanisms of action of these various disease risk loci can be grouped
into a number of specific categories.

Research Design and Methods

Contributing Studies

The following three partially overlapping collections of samples were used (Table 1, Supplementary Table 1): 1) nine studies with detailed physiologic basal and/or dynamic measures after oral glucose stimulation, from a total of 23,443
individuals; 2) twenty-nine studies, including up to 58,614 individuals, with fasting trait data available from the MAGIC genome-wide meta-analysis
(16); and 3) seven studies, including 4,180 subjects, with intravenous-derived measures of insulin sensitivity. All participants were
adult, nondiabetic (diabetes defined by clinical diagnosis, diabetes treatment, or fasting plasma glucose levels of ≥7.0 mmol/L),
and of European ancestry. Individuals with impaired FG levels or impaired glucose tolerance were maintained in the analyses.
Subjects provided informed consent, and all studies were approved by local ethics committees.

Given the wide range of sample sizes available for different traits, we divided these 14 phenotypes into two groups based
on a sample size cutoff of 10,000. This resulted in 10 “principal” traits with data from >10,000 individuals ([i]–[iii] above)
and 4 traits with data on fewer individuals ([iv]–[vi] above). We focused initial analyses on the principal traits.

Single Nucleotide Polymorphism Definition and Proxies

We included 37 of the 38 type 2 diabetes–associated loci documented in the DIAGRAM consortium meta-analysis (2). This subset of known risk variants was selected given their relatively large effects on type 2 diabetes risk (earliest
discovered variants), identification in individuals of European descent, and good representation on a genome-wide association
study or custom genotype panels within the contributing studies. Variants at the fat mass– and obesity-associated gene (FTO) were excluded given the well-documented primary association with BMI, which mediates the effect of FTO on type 2 diabetes risk (21,22). None of the other 37 loci has evidence for primary BMI associations. At these 37 loci, we included data for the lead single
nucleotide polymorphism (SNP) and a total of 126 alternative proxy SNPs. In studies where data for the lead SNP were not available,
we chose the best proxy SNP for each locus on a study-specific basis, using r2 measures from CEU HapMap (23) (Supplementary Table 1). Quality control and exclusion criteria were as previously described (14) (Supplementary Note, Supplementary Table 1). Not all samples had called genotypes on the sex chromosomes, and as a result the maximum sample size for chromosome X locus
DUSP9 was 7,642 individuals (i.e., <10,000). We thus excluded this locus from the main analyses.

Statistical Analysis

Linear regression was performed to test for association, under an additive genetic model, between SNPs and quantitative glycemic
traits adjusting for age, sex, and BMI within each cohort. Cohort-specific effect estimates and SEs derived from the regression
models were then combined in an inverse variance–weighted fixed-effects meta-analysis using GWAMA (24) or METAL (25). Associated P values are reported without correction for multiple testing. Two-sided P values <0.05 were considered to be significant given high prior probabilities for the association of established type 2 diabetes
risk loci (reported previously at a genome-wide significance level of P < 5 × 10−8) with glycemic traits (2,4,26,27). To investigate the impact of risk variants on physiologic traits, for selected trait pairs, we plotted the standardized
β-coefficient estimates of the effects to account for differences in trait transformations and the power of individual meta-analyses.

Cluster Analysis of Physiologic Traits and Type 2 Diabetes Loci

To explore the physiological basis of type 2 diabetes associations, we performed a primary cluster analysis using principal
traits only and a subsidiary analysis that included all 14 traits. We also performed a cluster analysis of the 36 loci (excluding
DUSP9), which grouped their effects on principal traits. We used meta-analysis z scores to perform complete linkage hierarchical clustering and aligned all effects to the disease risk–increasing allele.
In this type of cluster analysis, the distance between two clusters is computed as the maximum distance between a pair of
traits/SNPs that map in separate clusters (28). Locus clusters were defined by L2, a Euclidean distance dissimilarity measure. The uncertainty of hierarchical clustering was evaluated via multiscale bootstrap
resampling (29). Ten thousand bootstrap replicates were generated to compute a probability for the strength of support for each dendrogram
node and to evaluate topology sensitivity to sample size for each phenotype. We subsequently performed a centroid-based clustering
analysis to identify the most supported number of clusters, where the full set of SNPs could be structured. In this clustering
method, orthogonal transformation results in a reduced set of observations for each locus, translating 10 phenotypes to two
linearly uncorrelated principal components. Dendrograms were created where markers were forced into k groups (from two to eight), and the Calinski index (30) was computed as a measure of clustering support. We then performed principal component analysis on centroid-based clustering
results to visualize graphically the assignment of SNPs to inferred clusters.

Cluster Analyses

Complete linkage cluster analysis focusing on the structure of trait relationships (Supplementary Fig. 3, principal traits, and Supplementary Fig. 4, all traits) confirmed that traits grouped in a meaningful manner were consistent with expected physiological relationships.
We observed, for example, grouping of the four ISI measures and of HOMA-B and insulinogenic index. The Calinski index, which
determines the best partitioning and the optimal number of these trait clusters, was maximal at eight, indicating no large
subclusters (Supplementary Fig. 3B).

We next reclustered data, using principal traits only, to define relationships among the 36 type 2 diabetes loci (Fig. 1A). The unimodal distribution of the Calinski index was maximal at five, with the same number of groups emerging from an alternative
clustering analysis based on centroids (Supplementary Fig. 5). Group assignment for the optimal Nclusters determined using the Calinski index was coherent with results from linkage clustering. On this basis, we defined four locus
clusters with distinctive phenotypic features (Fig. 1B), which we termed as follows: IR; hyperglycemic (HG); PI processing; and BC. The twenty remaining loci not included in these
phenotypic clusters we describe as forming an unclassified (UC) group. In bootstrap resampling (Fig. 1A), the baseline branching nodes were highly supported (strength P ≥ 0.84, with best support for a node defined as Pmax = 1.00, and absence of support as Pmin = 0) with the only exception of the PI group where there was slightly less evidence of separation from the BC-UC clade (strength
P = 0.64).

Cluster analysis of effects of 36 type 2 diabetes loci on principal physiologic traits. Clustering of traits with meta-analysis
results from at least 10,000 individuals (principal traits). The existence of five clusters was revealed using two clustering
approaches. A: Complete linkage dendrogram of type 2 diabetes SNPs with P values (%) indicating the robustness of each branching event (shown in red). We named the clusters as HG loci linked to reduced
BC function after glucose stimulation, IR loci with a primary effect on IR at basal measurements, PI locus linked to decreased
fasting PI, BC loci associated with defective BC function, and UC loci with no apparent impact on glycemic measures. Strong
support exists for the baseline branching notes (strength P ≥ 0.84), whereas branching of IR from the BC-UC clade shows lesser evidence for support (strength P = 0.64). B: Calinski index computed on the centroid-based clustering of type 2 diabetes SNPs provides further evidence for the existence
of five locus groups.

Four Clusters of Type 2 Diabetes Risk Loci With Distinctive Phenotypic Features

Here we describe the features of each of the four clusters. To visualize some of these groupings, Fig. 2 and Supplementary Fig. 6 present selected scatter plots of physiological trait pairs. Associated P values reported in the text are selected for illustrative purposes with full data detailed in Table 2 and Supplementary Tables 1 and 5.

Scatter plots of standardized allelic effect size estimates for selected trait pairs. In each scatter plot, loci were assigned
to the groups defined from the cluster analysis of principal traits (groups highlighted by different colors). A: Insulinogenic index vs. FI: this plot highlights the effects of loci linked to IR (PPARG, KLF14, IRS1, and GCKR) with respect to FI and insulinogenic index. B: Insulinogenic index vs. FG: the plot reveals the largest impact of HG loci (MTNR1B and GCK) on FG driven by reduced BC function. Large negative effects on insulinogenic index are also seen for CDKAL1 and HHEX/IDE, but with very modest effects on FG. C: HOMA-B vs. HOMA-IR: the plot shows the separation of the BC, HG, and IR clusters. Cluster group colors are as follows: HG,
orange; IR, green; PI, pink; BC, red; UC, blue. Loci named in the box are coded numerically within the respective scatter
plot.

The IR cluster contains four loci (IRS1, GCKR, PPARG, and KLF14) and is characterized by the association between type 2 diabetes risk alleles and reduced insulin sensitivity, as evidenced
by higher HOMA-IR (IRS1 [P = 7 × 10−10], GCKR [P = 4 × 10−16], PPARG [P = 4 × 10−6], and KLF14 [P = 7 × 10−7]) and by reduced ISIs, with the strongest associations seen for decreased Matsuda ISI (IRS1 [P = 2 × 10−7], GCKR [P = 0.008], and PPARG [P = 0.004]). The IR loci also tended to show elevation of FG, FI, PI (IRS1 [P = 0.006], GCKR [P = 0.050]), and C-peptide levels (IRS1 [P = 0.01] and GCKR [P = 0.008]). We observed some evidence that carriers of risk alleles at these loci had higher values for HOMA-B (IRS1 [P = 3 × 10−7]) and insulinogenic index (IRS1 [P = 4 × 10−4]) (Fig. 2A and Supplementary Fig. 6A), which we interpret as likely to reflect, at least in truncated ascertainment among nondiabetic individuals (see DISCUSSION). Given access to data from the largest set of individuals yet characterized for intravenous indices of insulin sensitivity
(Nmax = 4,180), we specifically evaluated the effects of IR loci on these measures. We observed an association between the disease
risk variant at IRS1 and reduced intravenous measures of insulin sensitivity (P = 7 × 10−4) (Table 2), but no nominally significant associations were seen at GCKR, PPARG, or KLF14. Dropping either FI or HOMA-IR measures from the cluster analyses (these traits are highly correlated) had only subtle effects
on cluster definitions and relationships: for example, in analyses that retain FI but exclude HOMA-IR, three loci, KCNQ1 [rs231362], JAZF1, and HMGA2 moved from the UC group to the IR cluster. Adjustment for BMI improved estimates of both basal and dynamic measures of insulin
sensitivity (data not shown).

The nine loci in the BC cluster (TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B, THADA, DGKB, PROX1, and ADCY5) are characterized by reduced insulin secretion in the presence of increased PI and, in comparison with the HG cluster, only
modest increases in fasting glycemia. For all nine loci, we observed associations with lower levels of HOMA-B (Fig. 2C), higher levels of FG, and lower levels of FI (Table 2). The strongest associations were seen at TCF7L2, but all loci also showed similar patterns of reduced insulinogenic index (Fig. 2B) and AIR (Supplementary Fig. 6E), as well as increased levels of PI (Supplementary Fig. 6C). There was little systematic evidence for effects on HOMA or intravenous measures of insulin sensitivity (Supplementary Fig. 6F), though the type 2 diabetes risk allele at ADCY5 displayed reduced sensitivity on intravenous measures (P = 6 × 10−3) (Supplementary Fig. 6F).

We observed no material difference in the overall picture of trait associations when we compared results from this study with
those previously described by Ingelsson et al. (14) in partially overlapping samples at the eight loci examined in both.

Discussion

We present here the most comprehensive report to date concerning the role of type 2 diabetes risk loci on physiologic trait
variability in nondiabetic individuals. In addition, this study includes the largest number of individuals phenotyped using
gold standard intravenous-derived measures of insulin sensitivity. Through cluster analysis, we have shown that, in terms
of diabetes risk allele effect size on intermediate glycemic traits, type 2 diabetes risk loci fall into five broad categories.

The IR cluster loci, including those mapping near PPARG, KLF14, IRS1, and GCKR, are characterized by consistent effects across multiple indices of insulin sensitivity, in both the basal and stimulated
state. Three of the four IR loci (PPARG excluded) ranked in the lower half of type 2 diabetes risk loci in terms of effect size: this may indicate that some of the
loci in the UC group act through weak effects on insulin sensitivity (as has indeed been suggested for HMGA2) (2). The observation that disease risk alleles at IR loci are associated with some degree of improved BC function (such as that
measured by HOMA-B) is likely to reflect a combination of complementary factors. An increase in insulin secretion as a result
of higher IR is a known physiological phenomenon, which occurs until individuals decompensate and shift toward the development
of diabetes. Therefore, alleles that primarily raise IR may be secondarily associated with improved insulin secretion through
a compensatory mechanism. In addition, the apparent association with insulin sensitivity may in part reflect the fact that
analysis was restricted to nondiabetic subjects: this can lead to a truncation effect, whereby individuals with genotypes
that favor both high IR and poor insulin secretion are preferentially depleted from the analysis because of their diabetes
predisposition (6).

The PI cluster is limited to the type 2 diabetes risk variant at ARAP1, with the cardinal feature being a marked reduction in fasting PI levels in carriers of the disease risk allele. The combination
of diabetes risk reduced basal and stimulated insulin secretion and reduced PI levels runs counter to the usual epidemiological
associations (7). Our data therefore support previous assertions that the ARAP1 risk variant increases the risk of type 2 diabetes through defects in the early steps of insulin production (7).

The HG cluster includes loci characterized by a prominent reduction in basal and stimulated BC function (35–37), resulting in a marked increase in FG levels. This glycemic effect is consistent with the fact that the type 2 diabetes
risk alleles at these loci are associated with modest reductions in basal insulin sensitivity (as measured by HOMA-IR). These
associations with insulin action phenotypes did not, however, extend to the stimulated measures and may simply reflect limitations
in the HOMA model. This cluster includes both GCK and MTNR1B, and, although they share similar features in terms of their effects on FG, HOMA-B, and insulin/glucagon ratios (11), external data suggest that these genes act through different mechanisms. In individuals with maturity onset diabetes of
the young type 2 (38), severe loss-of-function mutations in GCK lead to impaired glucose sensing and a higher homeostatic set point for glucose. At MTNR1B, several studies have shown that lower insulin secretion is accompanied by markedly reduced insulin sensitivity (11) and increased insulin response to glucagon-like peptide 1 (39). The apparent lack of effects on PI measures for MTNR1B, as for GCK, points to an effect on the secretory function of BCs, rather than on PI processing (14).

The BC cluster includes a number of loci associated with defective insulin secretion but without the marked glycemic effects
seen in the HG cluster. These include several of the loci with the strongest allelic effects on diabetes risk, such as those
at TCF7L2, SLC30A8, CDKAL1, and CDKN2A/2B, suggesting that the distinction from the loci in HG cluster is not simply a consequence of the degree of allelic impact.
Although the BC loci map to a single cluster, there is a degree of heterogeneity with respect to other traits. For example,
disease risk alleles at TCF7L2 and SLC30A8 are associated with increased PI levels, reduced insulinogenic index, and lower FI levels, underscoring defects in insulin
processing and secretion (7). In contrast, the risk alleles at HHEX/IDE, DGKB, CDKAL1, PROX1, and CDKN2A/2B display reduced insulinogenic index and AIR, with no effect on fasting PI levels, suggesting defects during the first phase
of insulin response and early insulin secretion. These phenotypic associations within the BC cluster highlight the potential
for further subdivision of loci according to pathophysiological patterns.

The UC group combines all remaining loci, which, although they are genome-wide significant for type 2 diabetes, display no
discernible impact on glycemic measures across the large sample sets assembled here. Most of these loci rank in the lower
half of signals in terms of type 2 diabetes effect size, and modest functional impact may offer a partial explanation for
this observation. However, it is notable that the UC group includes many of the loci where the common variant effects are
highly likely to be mediated by transcripts implicated in monogenic and syndromic forms of diabetes (including HNF1A, HNF1B [40], KCNJ11 [41], and WFS1 [42]). Thus, even in the setting where rare coding mutations result in severe abrogation of islet function, and common variants
acting through those same genes influence type 2 diabetes risk to stringent levels of significance, those same common variants
can also be compatible with normal glucose homeostasis (2).

This study combined information from a large number of individuals phenotyped using intravenous measures of insulin sensitivity,
including >2,600 subjects examined using the gold standard euglycemic-hyperinsulinemic clamp. Overall, the data do not support
the view that the combination of intravenous measures used in this study offers a sufficient boost in precision to compensate
for the reduction in sample size when compared with the numbers available for the basal or OGTT-derived measures. Except at
IRS1 and ADCY5, neither the intravenous measures nor the OGTT-derived ISIs generated powerful signals of association with deficient insulin
action, even at loci where the existing evidence for an effect on insulin sensitivity is compelling (e.g., PPARG, KLF14 [2,43]).

The study presented here is the largest investigation of its kind published to date; we set out to maximize the sample size
for each trait included. One consequence of this strategy is that the characteristics of the particular samples informative
for each trait may differ, raising the possibility of artifacts when comparing genotypic associations across traits. However,
the extent to which our findings are both internally consistent, and in broad agreement with published data describing more
detailed phenotypic studies of individual variants, provides considerable reassurance. An alternative strategy, which restricted
analyses to a small core of individuals with data available for all traits, would have resulted in a substantial loss of power.

We recognize some other limitations of this study, and of others of its kind. First, the complexity of the metabolic phenotypes
examined, the longitudinal dimension of diabetes development and progression, and the relative imprecision of the experimental
tools at our disposal limit the inferences that are possible. This is most obviously seen in the large numbers of loci, which
though having genome-wide significance for type 2 diabetes, show no evidence of a relationship with intermediate metabolic
traits. We also note, for example, that the measures of early insulin secretion used (insulinogenic index and AIR) may, in
part, reflect insulin clearance, a trait that was not evaluated directly. Second, in the interests of avoiding the secondary
effects of diabetes and its treatment, analyses were restricted to nondiabetic individuals: this is likely to have introduced
some truncation effects such as the apparent enhancement of BC function for disease risk alleles disrupting insulin sensitivity.
These have the potential to lead to incorrect inference, if not recognized as such (6,44). Third, the variants we have examined are not, in most cases, known to be causal for the association signals detected, leaving
open the possibility that, at some loci, the causal alleles, once identified, may have more pronounced effects on intermediate
traits than the variants studied here. Finally, additional recently published disease associations (3) of variants with lesser genetic effects were not considered. Future efforts to extend our study to incorporate these variants
will likely require substantially larger sample sizes.

In summary, in this examination of the intermediate metabolic phenotypic associations of proven type 2 diabetes risk alleles,
we have demonstrated that the loci fall into a limited number of broad mechanistic categories. These highlight the diverse
mechanisms contributing to individual risk of disease. These data will guide future experimental studies that will use more
specific, tailored tests to further dissect these key pathogenetic processes.

Article Information

Acknowledgments. The authors thank the many Finnish volunteers who generously participated in the Finland-United States Investigation of NIDDM
Genetics, D2D, Health 2000, Finrisk 1987, Finrisk 2002, and Savitaipale studies from which we chose our FUSION GWA and replication
cohorts (no overlap with Health 2000 replication cohort). The authors also thank Terry Gliedt and Peggy White for informatics
and analysis support. The Center for Inherited Disease Research performed the GWA genotyping. The authors thank all subjects
who participated in the Metabolisches Syndrom Berlin Potsdam Study; Marianne Deweider and Fredéric Allegaert for the DNA bank
management; Philippe Gallina, Philippe Delfosse, and Sylvie Poulain for the recruitment of most obese adults; Department of
Nutrition of Paris Hotel Dieu Hospital for the contribution to the recruitment effort; and Stéphane Lobbens for genotyping.
The authors thank Dr. Sekar Kathiresan at the Massachusetts General Hospital and Broad Institute of Harvard and MIT for his
collaboration in assembling the National Health and Nutrition Examination Survey (NHANES) DNA samples, and Jody E. McLean
at the Division of Health and Nutrition Examination Surveys, National Center for Health Statistics, Centers for Disease Control
and Prevention, for her assistance with the analysis of NHANES genetic data. The authors thank Roche Pharmaceuticals for its
support and collaboration in assembling the Partners/Roche Cohort. The Genotyping for the Prospective Investigation of the
Vasculature in Uppsala Seniors and the Uppsala Longitudinal Study of Adult Men were performed by the SNP&SEQ Technology Platform
in Uppsala, Sweden (www.genotyping.se). The authors thank Tomas Axelsson, Ann-Christine Wiman, and Caisa Pöntinen for their excellent assistance with genotyping.
The SNP Technology Platform is supported by Uppsala University, Uppsala University Hospital, and the Swedish Research Council
for Infrastructures. The authors thank Knut Krohn (Microarray Core Facility of the Interdisciplinary Centre for Clinical Research,
University of Leipzig) for the genotyping/analytical support and Joachim Thiery (Institute of Laboratory Medicine, Clinical
Chemistry and Molecular Diagnostics, University of Leipzig) for clinical chemistry services. The authors thank Nigel W. Rayner
(Wellcome Trust Centre for Human Genetics, University of Oxford) for excellent bioinformatics support.

Funding. The major funding for this meta-analysis was provided through the Seventh Framework Programme of the European Community (FP7/2007-2013),
the ENGAGE project, and grant agreement HEALTH-F4-2007-201413. As part of the Ely Study, I.B. was supported by Wellcome Trust
grant WT098051, the Cambridge Biomedical Research Centre of the U.K. National Institute for Health Research, and the Medical
Research Council Centre for Obesity and Related Metabolic Diseases. The EUGENE2 Study was supported by grant LSHM-CT-2004-512013
from the European Community FP6 EUGENE2. The Framingham Heart Study was conducted in part using data and resources from the
Framingham Heart Study of the National Heart, Lung, and Blood Institute of the National Institutes of Health (NIH) and Boston
University School of Medicine. The analyses reflect intellectual input and resource development from the Framingham Heart
Study investigators participating in the SNP Health Association Resource project. This work was partially supported by the
National Heart, Lung, and Blood Institute's Framingham Heart Study (contract no. N01-HC-25195) and its contract with Affymetrix
Inc. for genotyping services (contract no. N02-HL-6-4278). A portion of this research utilized the Linux Cluster for Genetic
Analysis (LinGA-II), which is funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University
School of Medicine and Boston Medical Center. The Framingham Heart Study was also supported by National Institute of Diabetes
and Digestive and Kidney Diseases (NIDDK) grant R01 DK-078616 to J.B.M., J.D., and J.C.F.; NIDDK grant K24 DK-080140 to J.B.M.;
and a Doris Duke Charitable Foundation Clinical Scientist Development Award to J.C.F. Support for the Finland-United States
Investigation of NIDDM Genetics was provided by NIH grants DK-069922 (to R.M.W.), U54 DA021519 (to R.M.W.), DK-062370 (to
M.B.), and DK-072193 (to K.L.M.). Additional support comes from the National Human Genome Research Institute intramural project
number 1 Z01 HG000024 (to F.S.C.). The Metabolisches Syndrom Berlin Potsdam (MESYBEPO) Study was supported by grants from
Agence Nationale de la Recherche and Conseil Regional Nord-Pas de Calais/Fonds européen de développement économique et régional.
Support for the Metabolic Syndrome in Men Study was provided by grant 124243 from the Academy of Finland. In the MESYBEPO
Study, J.S. was supported by Clinical Research Group grant KFO218/1, Heisenberg Professorship SP716/2-1 of the Deutsche Forschungsgemeinschaft
(DFG), and a research group on molecular nutrition of the German Bundesministerium für Bildung und Forschung (BMBF). The Partners/Roche
Cohort was supported by the Mallinckrodt General Clinical Research Program, National Center for Research Resources, and NIH
grant RR01066. In the Prospective Investigation of the Vasculature in Uppsala Seniors and the Uppsala Longitudinal Study of
Adult Men, the SNP Technology Platform is supported by Uppsala University, Uppsala University Hospital, and the Swedish Research
Council for Infrastructures. E.I. was supported by grants from the Swedish Research Council, the Swedish Heart-Lung Foundation,
the Swedish Foundation for Strategic Research, and the Royal Swedish Academy of Science. The QTL families and UNG92 Study
is supported by the Lundbeck Foundation Centre of Applied Medical Genomics for Personalized Disease Prediction, Prevention,
and Care, The Danish Diabetes Association, and The Danish Research Council. The Relationship between Insulin Sensitivity and
Cardiovascular Disease Study is supported by European Union grant QLG1-CT-2001-01252 and AstraZeneca. The Sorbs Study was
supported by grants from the German Research Council (SFB-1052 “Obesity mechanisms”) and the German Diabetes Association and
the Diabetes Hilfs und Forschungsfonds Deutschland. P.K. is funded by the Boehringer Ingelheim Foundation. The Integrated
Research and Treatment Center Adiposity Diseases is supported by BMBF grant FKZ: 01EO1001. I.P. and V.L. were partially funded
through the Seventh Framework Programme of the European Community (grant FP7/2007-2013) and the ENGAGE project grant agreement
HEALTH-F4-2007-201413. R.M. is funded by the European Commission under the Marie Curie Intra-European Fellowship and a European
Foundation for the Study of Diabetes New Horizons grant. M.I.M. is supported by Wellcome Trust grant 098381. Sample collection
for the Stanford IST Cohort was carried out in part in the Clinical and Translational Research Unit, Stanford University,
with funds provided by the National Center for Research Resources, NIH grant 2 M01 RR000070, and the U.S. Public Health Service.
J.W.K. is supported by American Heart Association Fellow to Faculty Transition award 10FTF3360005.

Duality of Interest. J.B.M. currently serves on a consultancy board for Interleukin Genetics. R.M.W. has received consulting honoraria and receives
research funding from Merck & Co., and receives research material support from Takeda Pharmaceuticals North America. J.C.F.
has received consulting honoraria from Daiichi-Sankyo and AstraZeneca. I.B. and spouse own stock in GlaxoSmithKline and Incyte.
No other potential conflicts of interest relevant to this article were reported.